Clustering Distributed Sensor Data Streams

• Type: Article in International Scientific Journal
• Date: 2008

Authors

Pedro Pereira Rodrigues (Author) FCUP; Joao Gama (Author) FEP; Luis Lopes (Author) FCUP

Journal

Title: Lecture Notes in Computer Science

Serial No. PART 2 Vol. 521

Pages: 282-297

ISSN: 0302-9743

Publisher: Springer Nature

Indexing

ISI Proceedings

ISI Web of Knowledge - 10 Citations

Scopus - 13 Citations

Scientific classification

FOS: Natural sciences > Computer and information sciences

CORDIS: Physical sciences > Computer science

Other information

Authenticus ID: P-004-423

DOI: 10.1007/978-3-540-87481-2_19

Abstract (EN): Nowadays applications produce infinite streams of data distributed across wide sensor networks. In this work we study the problem of continuously maintain a cluster structure over the data points generated by the entire network. Usual techniques operate by forwarding and concentrating the entire data in a central server, processing it as a multivariate stream. In this paper, we propose DGClust, a new distributed algorithm which reduces both the dimensionality and the communication burdens, by allowing each local sensor to keep an online discretization of its data stream, which operates with constant update time and (almost) fixed space. Each new data point triggers a cell in this univariate grid, reflecting the current state of the data stream at the local site. Whenever a local site changes its state, it notifies the central server about the new state it is in. This way, at each point in time, the central site has the global multivariate state of the entire network. To avoid monitoring all possible states, which is exponential in the number of sensors, the central site keeps a small list of counters of the most frequent global states. Finally, a simple adaptive partitional clustering algorithm is applied to the frequent states central points in order to provide an anytime definition of the clusters centers. The approach is evaluated in the context of distributed sensor networks, presenting both empirical and theoretical evidence of its advantages.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 16